Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts
Benjamin Meyer,Boodsarin Sawatlon,Stefan Heinen,Stefan Heinen,O. Anatole von Lilienfeld,O. Anatole von Lilienfeld,Clémence Corminboeuf +6 more
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The application of modern machine learning to challenges in atomistic simulation is gaining attraction and the potential for innovation in this area is being explored.Abstract:
The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C–C cross-coupling reactions. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots. The versatility of this approach is illustrated for vast libraries of organometallic catalysts based on Pt, Pd, Ni, Cu, Ag, and Au combined with 91 ligands. Out-of-sample machine learning predictions were made on a total of 18 062 compounds leading to 557 catalyst candidates falling into the ideal thermodynamic window. This number was further refined by searching for candidates with an estimated price lower than 10 US$ per mmol. The 37 catalyst finalists are dominated by palladium phosphine ligand combinations but also include the earth abundant transition metal (Cu) with less common ligands. Our results indicate that modern statistical learning techniques can be applied to the computational discovery of readily available and promising catalyst candidates.read more
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References
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Journal ArticleDOI
Improving the Thermodynamic Profiles of Prospective Suzuki–Miyaura Cross‐Coupling Catalysts by Altering the Electrophilic Coupling Component
TL;DR: In this paper, the influence of the electrophilic coupling component in catalytic cycle thermodynamics is revealed by using molecular volcano plots, which shows that less reactive electrophiles, such as iodine, broaden the volcano plateau, which leads to a larger number of catalysts having appealing thermodynamic profiles.
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New tricks by very old dogs: predicting the catalytic hydrogenation of HMF derivatives using Slater-type orbitals
TL;DR: In this paper, the authors reported new experimental results on the hydrogenation of 5-ethoxymethylfurfural, an important intermediate in the conversion of sugars to industrial chemicals, using eight M/Al2O3 catalysts (M = Au, Cu, Ni, Ir, Pd, Pt, Rh, and Ru) under various conditions.
Book
Understanding Organometallic Reaction Mechanisms and Catalysis: Computational and Experimental Tools
TL;DR: In this article, the latest insights and developments in the mechanistic studies of organometallic reactions and catalytic processes are presented and reviewed, exemplifying how to use experiments, spectroscopy measurements, and computational methods to reveal reaction pathways and molecular structures of catalysts, rather than concentrating solely on one discipline.